Baseline approach: Reinforcement Learning (RL) is widely used in NAS [1]}, [2]}, [3]}; hence we have implemented a RL-based NAS framework as a baseline.
For the baseline, we consider the objective function in Equation REF .
Specifically, we incorporate a deep-Q network approach for the baseline-RL [4]}.
We construct four different controllers for the number of cell (\(N_c\) ), cell depth (\(d_c\) ), width multiplier (\(w_m\) ) and number of long skip connections (\(SC_c\) ).
The training hyper-parameters for the baseline-RL are shown in Table REF .
The baseline-RL approach estimates the optimal parameters (\(N_c, d_c, w_m, SC_c\) ).
We tune the baseline-RL approach to obtain the best possible results. We also implement a one-level SHGO algorithm (i.e., original SHGO) as another baseline to show the efficiency of our hierarchical algorithm.
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